scholarly journals Survey on Mutation-based Test Data Generation

2015 ◽  
Vol 5 (5) ◽  
pp. 1164
Author(s):  
Hanh Le Thi My ◽  
Binh Nguyen Thanh ◽  
Tung Khuat Thanh
Keyword(s):  
Test Data ◽  
Test Cases ◽  
Generation Process ◽  
Test Data Generation ◽  
Data Generation ◽  
Comprehensive Survey ◽  
Critical Activity ◽  
Automatic Test Data Generation ◽  
Systematic Selection ◽  
Selection Of

<span>The critical activity of testing is the systematic selection of suitable test cases, which be able to reveal highly the faults. Therefore, mutation coverage is an effective criterion for generating test data. Since the test data generation process is very labor intensive, time-consuming and error-prone when done manually, the automation of this process is highly aspired. The researches about automatic test data generation contributed a set of tools, approaches, development and empirical results. In this paper, we will analyse and conduct a comprehensive survey on generating test data based on mutation. The paper also analyses the trends in this field.</span>

scholarly journals A Sound Assessment of Test Suite Minimization Techniques

2018 ◽  
Vol 7 (2) ◽  
pp. 87-91
Author(s):  
Fayaz Ahmad Khan
Keyword(s):  
Software Development ◽  
Test Data ◽  
Comprehensive Analysis ◽  
Test Suite ◽  
Test Cases ◽  
Test Data Generation ◽  
Data Generation ◽  
Before And After ◽  
New Ideas ◽  
Automatic Test Data Generation

During software development, testing and re-testing occurs frequently to ensure that the software is working correctly before and after modifications. To carry out an effective testing process a test suite is created and executed to detect the faults in the existing code as well as in the modified code. The manual approach of test suite creation and execution is time consuming and labour intensive task as compared to automatically generated test data or test suite. The automatic test data generation is supposed to be an effective way, but a lot of redundant test cases are generated that increase the time, effort and cost of testing. Therefore, test suite minimization techniques are used to further minimize or reduce the number of test cases by selecting a subset from an initially random and large test suite to test the code before as well as after modification. In this study, a comprehensive analysis of the different test suite minimization techniques is presented in order to extend the existing studies and to propose new ideas in this direction.

Test Data Generation Based on Test Path Discovery Using Intelligent Water Drop

2012 ◽  
Vol 3 (2) ◽  
pp. 56-74 ◽  
Author(s):  
Praveen Ranjan Srivastava ◽  
Amitkumar Patel ◽  
Kunal Patel ◽  
Prateek Vijaywargiya
Keyword(s):  
Test Data ◽  
Water Drop ◽  
Automatic Generation ◽  
Control Flow ◽  
Test Cases ◽  
Test Data Generation ◽  
Data Generation ◽  
Path Coverage ◽  
Automatic Test Data Generation ◽  
Generate Test Data

Automatic test data generation is required to generate test cases dynamically for a specific software program. Manual generation of test data is too tedious and a time consuming task. This paper proposes a technique using Intelligent Water Drop (IWD) for automatic generation of test data. Correctly generated test data helps in reducing the effort while testing the software. This paper discusses different algorithms based on IWD to generate test data and path coverage over Control Flow Graph. Test data is generated keeping in mind all of the programming constraints like “if,” “while,” “do while,” etc., available in the program.

scholarly journals Software Test Data Generation based on Path Testing using Genetic Algorithms

2020 ◽  
Vol 8 (6) ◽  
pp. 4466-4473
Keyword(s):  
Genetic Algorithms ◽  
Test Data ◽  
Iterative Algorithms ◽  
Continuous Functions ◽  
Software Test ◽  
Test Cases ◽  
Test Data Generation ◽  
Data Generation ◽  
Optimal Solutions ◽  
Automated Test

Test data generation is the task of constructing test cases for predicting the acceptability of novel or updated software. Test data could be the original test suite taken from previous run or imitation data generated afresh specifically for this purpose. The simplest way of generating test data is done randomly but such test cases may not be competent enough in detecting all defects and bugs. In contrast, test cases can also be generated automatically and this has a number of advantages over the conventional manual method. Genetic Algorithms, one of the automation techniques, are iterative algorithms and apply basic operations repeatedly in greed for optimal solutions or in this case, test data. By finding out the most error-prone path using such test cases one can reduce the software development cost and improve the testing efficiency. During the evolution process such algorithms pass on the better traits to the next generations and when applied to generations of software test data they produce test cases that are closer to optimal solutions. Most of the automated test data generators developed so far work well only for continuous functions. In this study, we have used Genetic Algorithms to develop a tool and named it TG-GA (Test Data Generation using Genetic Algorithms) that searches for test data in a discontinuous space. The goal of the work is to analyze the effectiveness of Genetic Algorithms in automated test data generation and to compare its performance over random sampling particularly for discontinuous spaces.

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